MobileNet-V2 and IGCV3

An implementation of Google MobileNet-V2 and IGCV3 introduced in PyTorch. Link to the original paper: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation,IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. arXiv preprint arXIV:1806.00178 (2017)

This implementation was made to be an example of a common deep learning software architecture. It's simple and designed to be very modular. All of the components needed for training and visualization are added.

Only tested on Cifar10 and Cifar100!!!!

Usage

This project uses Python 3 and PyTorch 0.3.1

Main Dependencies

pytorch 0.3.1
numpy 
tqdm 
easydict
matplotlib 
tensorboardX

Install dependencies:

pip install -r requirements.txt

Train and Test

Prepare your data, then create a dataloader class such as cifar10data.py and cifar100data.py.
Create a .json config file for your experiments. Use the given .json config files as a reference.

Run

python main.py config/<your-config-json-file>.json

Experiments

Due to the lack of computational power. I trained on CIFAR-10 dataset as an example to prove correctness, and was able to achieve test top1-accuracy of 90.9%.

Tensorboard Visualization

Tensorboard is integrated with the project using tensorboardX library which proved to be very useful as there is no official visualization library in pytorch.

You can start it using:

tensorboard --logdir experimenets/<config-name>/summaries

These are the learning curves for the CIFAR-10 experiment.

IGCV3:Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks.

The codes are based on https://github.com/liangfu/mxnet-mobilenet-v2.

IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. arXiv preprint arXIV:1806.00178 (2017)

Prior Works

Interleaved Group Convolutions (IGCV1)

Interleaved Group Convolutions use a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary.

Illustrating the interleaved group convolution, with L = 2 primary partitions and M = 3 secondary partitions. The convolution for each primary partition in primary group convolution is spatial. The convolution for each secondary partition in secondary group convolution is point-wise (1 × 1).

You can find its code here!

Interleaved Structured Sparse Convolution (IGCV2)

IGCV2 extends IGCV1 by decomposing the convolution matrix in to more structured sparse matrices, which uses a depth-wise convoultion (3 × 3) to replace the primary group convoution in IGC and uses a series of point-wise group convolutions (1 × 1).

Interleaved Low-Rank Group Convolutions (IGCV3)

We proposes Interleaved Low-Rank Group Convolutions, named IGCV3, extend IGCV2 by using low-rank group convolutions to replace group convoutions in IGCV2. It consists of a channel-wise spatial convolution, a low-rank group convolution with $G_{1}$ groups that reduces the width and a low-rank group convolution with $G_{2}$ groups which expands the widths back.

Illustrating the interleaved branches in IGCV3 block. The first group convolution is a group 1 × 1 convolution with $G_{1}$ =2 groups. The second is a channel-wise spatial convolution. The third is a group 1 × 1 convolution with $G_{2}$ =2 groups.

Results

CIFAR Experiments

We compare our IGCV3 network with other Mobile Networks on CIFAR datasets which illustrated our model' advantages on small dataset.

Comparison with Other Mobile Networks

Classification accuracy comparisons of MobileNetV2 and IGCV3 on CIFAR datasets. "Network s×" means reducing the number of parameter in "Network 1.0×" by s times.

	#Params (M)	CIFAR-10	CIFAR100
MobileNetV2（our impl.）	2.3	94.56	77.09
IGCV3-D 0.5×	1.2	94.73	77.29
IGCV3-D 0.7×	1.7	94.92	77.83
IGCV3-D 1.0×	2.4	94.96	77.95
IGCV3-D 1.0×(my pytorch impl)	2.4	94.70	75.96
MobileNetV2（my pytorch impl）	2.3	94.01	--

Comparison with IGCV2

	#Params (M)	CIFAR-10	CIFAR100
IGCV2	2.4	94.76	77.45
IGCV3-D	2.4	94.96	77.95

ImageNet Experiments

Comparison with MobileNetV2 on ImageNet.

Before Retrain

	#Params (M)	Top-1	Top-5
MobileNetV2	3.4	70.0	89.0
IGCV3-D	3.5	70.6	89.7

After Retrain

	#Params (M)	Top-1	Top-5
MobileNetV2	3.4	71.4	90.1
IGCV3-D	3.5	72.2	90.5

The code is maily from IGCV3 and MobilenetV2.Thanks for their contribution.

zhangxu0307 / igcv3-pytorch Goto Github PK

igcv3-pytorch's Introduction